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Serialization

This notebook walks through how to write and read an LLM Configuration to and from disk. This is useful if you want to save the configuration for a given LLM (e.g., the provider, the temperature, etc).

from langchain.llms import OpenAI
from langchain.llms.loading import load_llm

API Reference:

Loading​

First, lets go over loading an LLM from disk. LLMs can be saved on disk in two formats: json or yaml. No matter the extension, they are loaded in the same way.

cat llm.json
    {
        "model_name": "text-davinci-003",
        "temperature": 0.7,
        "max_tokens": 256,
        "top_p": 1.0,
        "frequency_penalty": 0.0,
        "presence_penalty": 0.0,
        "n": 1,
        "best_of": 1,
        "request_timeout": null,
        "_type": "openai"
    }
llm = load_llm("llm.json")
cat llm.yaml
    _type: openai
    best_of: 1
    frequency_penalty: 0.0
    max_tokens: 256
    model_name: text-davinci-003
    n: 1
    presence_penalty: 0.0
    request_timeout: null
    temperature: 0.7
    top_p: 1.0
llm = load_llm("llm.yaml")

Saving​

If you want to go from an LLM in memory to a serialized version of it, you can do so easily by calling the .save method. Again, this supports both json and yaml.

llm.save("llm.json")
llm.save("llm.yaml")